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Как мы построили модель прогнозирования выхода жестких дисков из строя

Неожиданный выход HDD из строя — неприятная для сервера ситуация. Выяснение причин и замена жесткого диска (не всегда это можно сделать «горячим» способом) почти всегда означают даунтайм работы системы. При этом подсказок о своем состоянии HDD не дает, специалисты могут ориентироваться только на время эксплуатации диска и свой опыт.

Меня зовут Владислав Маркин, я инженер по разработке ПО искусственного интеллекта в YADRO. Вместе с моим коллегой, экспертом Андреем Соколовым, мы решили применить возможности ИИ в прогнозировании проблем с HDD. Задача не тривиальная: модели нужны данные для обучения и тренировки, а где их найти — отдельный вопрос.

В статье расскажу, что нам удалось сделать, что стало основой прототипа нашей прогнозной модели и какие результаты она показала в применении для дисков в серверах YADRO.

На основе каких данных можно прогнозировать неисправность HDD

Для жестких дисков существует довольно известный способ диагностики его состояния — S.M.A.R.T.-тестирование (от англ. self-monitoring, analysis and reporting technology — технология самоконтроля, анализа и отчетности). В результате теста мы получаем диагностическую информацию в виде набора числовых атрибутов. К ним относятся счетчики ошибок, показания датчиков температуры и другие.

Как может выглядеть вывод SMART-данных. Источник

Если в каком-то значении есть отклонение от нормы, оно будет подсвечено красным. Возможно, именно здесь кроется проблема нештатной работы HDD.

Всего существует порядка 255 идентификаторов, среди которых наработка часов, частота возникновения ошибок при позиционировании блока магнитных головок, количество повторных запусков шпинделя при неудачной первой попытке, количество неисправных секторов и многие другие. У разных моделей дисков может быть разное количество и список атрибутов — по сути, его определяет производитель: какую именно информацию о диске он считает нужным представить. В среднем диск сообщает до 20 атрибутов.

При достаточном количестве SMART-данных с некоторого множества дисков и исторических данных об их фактических отказах можно построить модель, которая давала бы прогноз, выйдет ли жесткий диск из строя в ближайшем будущем или нет.

Где взять набор SMART-данных

Американская компания по облачному хранению и резервному копированию данных Backblaze c 2013 года публикует SMART-данные, которые собирает в собственных дата-центрах. По сути, на данный момент это самый известный публичный датасет с такими данными. За 2020 и 2023 годы есть SMART-данные, собранные с 85 моделей дисков, подключаемых по интерфейсу SATA. По 14 из этих моделей собирались данные по более 10 000 штучных их представителей. То есть данных на самом деле немало.

И самое главное — этот датасет размечен. Есть столбец Failure, и единица в этом столбце означает последний день жизни диска.

Кроме этого датасета, мы нашли результаты соревнований PAKDD2020 Alibaba AI Ops Competition от компании Alibaba Cloud. Перед участниками стояла, по сути, аналогичная задача. Им нужно было построить ML-модель, которая формировала бы прогнозы вида «выйдет ли диск из строя в течение 30 дней или нет». Прогноз нужно было формировать раз в сутки для каждого диска.

Используемый участниками датасет похож на данные от BackBlaze — те же записи со SMART-атрибутами. Снимки SMART-данных делались с каждого диска раз в сутки, а данные представлены по двум моделям дисков. По условиям конкурса, к сожалению, информация о производителе и моделях дисков была закрыта.

Датасет, используемый в конкурсе, также был размечен, но информацию об отказах представляли в отдельном файле в виде failure tickets. Там был идентификатор диска и время его отказа.

Мы нашли код решения участников, которые заняли третье место, — вот их Jupyter Notebook. Его мы взяли за основу нашего прототипа. Также организаторы соревнования предложили метрики для оценки качества ML-моделей, мы переняли эти критерии, уточнив их.

Мы ищем коллег разных специальностей для работы с ПО искусственного интеллекта. Возможно, вас заинтересуют вакансии:
→ Старший разработчик (Full-Stack)
→ Техлид в области AI (LLM)
→ DevOps-инженер

Метрики качества ML-модели для прогнозирования выхода из строя HDD

Метрики мы назвали k-window в честь одного из параметров, о котором я расскажу ниже. В целом, это «классические» Precision (точность), Recall (полнота) и F1, но с некоторыми оговорками.

Precision — это точность, доля истинно позитивных прогнозов среди всех положительных прогнозов модели. В нашем случае прогнозом считается ответ на вопрос, выйдет ли конкретный диск из строя в ближайшее время или нет. Так, Precision в 0,7 будет означать следующее: если модель спрогнозировала выход из строя 100 дисков, то на самом деле только 70 из них действительно выйдут из строя в ближайшее время.

$Precision=\frac{TP}{(TP+FP)}$

Recall — это полнота, доля реальных отказов, которые модель смогла спрогнозировать. Допустим, на тестовых данных известно, что откажут 100 дисков. Recall в 0,4 будет означать, что на самом деле модель смогла спрогнозировать только 40 из сотни отказов. Остальные либо спрогнозировала несвоевременно, либо вообще пропустила, не подав никакой индикации.

$Recall=\frac{TP}{(All\quad Ground\quad Truth\quad Positive)}$

F1 — это гармоническое среднее между Precision и Recall.

$F1=2\ast \frac{Precision\ast Recall}{Precision+Recall}$

Параметр k (количество дней или часов) определяет ширину временного окна с момента вызова модели, в который должно попасть событие реального отказа. Чтобы лучше понять значение этого параметра, посмотрим на картинку ниже. На ней изображено, как при подсчете метрик интерпретируются прогнозы модели для одного диска.

Какие у нас могут быть сценарии прогнозов

Рассмотрим, опираясь на таймлайн, — начнем снизу.

Своевременно спрогнозировали выход диска из строя. В некий момент времени (значение ) модель делает позитивный прогноз. До этого позитивных прогнозов она не делала, это First positive prediction. В реальности, а не на картинке это было бы некой красной лампочкой на дашборде мониторинга у сисадмина или алертом в системе. То, что подразумевает: нужно среагировать на потенциальный выход из строя жесткого диска и предпринять какие-то действия.

От этого момента времени в будущее мы откладываем некий отрезок длины k (T + k) и смотрим, попало ли в него событие реального отказа. На изображении это зеленая область. True Positive прогноз в нее попал, а значит, мы своевременно спрогнозировали выход из строя.

Слишком поздно прогнозировали выход из строя. У этих метрик есть еще один параметр, который называется Best Before. Я не говорил про него ранее, потому что в моей задаче этот параметр пока не используется и равен нулю. Но в общем случае Best Before определяет зазор, который определяет некое минимальное время, когда прогноз еще будет актуальным.

Например, для пользователя модели прогноз, сделанный за сутки до отказа, — это слишком поздно — не успеет среагировать. В таком случае параметр Best Before должен быть больше — допустим, 48 часов.

Слишком рано спрогнозировали выход из строя. Опять-таки, модель делает первый позитивный прогноз с момента времени T, а событие реального отказа вывалилось за правую границу Т+k . Это тоже False Positive прогноз.

Ложно положительный прогноз, или False Positive. Модель сделала позитивный прогноз, но на тестовых данных реального отказа не было. То есть диск здоров, а мы фактически поставили на нем крест.

Ложно отрицательный прогноз, или False Negative. Тут примерно то же самое, что в предыдущем варианте, только в обратную сторону. Модель не сделала ни одного позитивного прогноза, но нам достоверно известно, что диск выйдет из строя и когда это произойдет.

Unknown Positive. Этот сценарий подразумевает случаи, когда модель сделала позитивный прогноз, но тестовые данные заканчиваются раньше, чем наступает правая граница окна T+k . То есть мы не можем однозначно сказать, что, например, прогноз был сделан слишком рано, так как у нас нет информации о времени реального отказа. Следовательно, нам не отнести это позитивное срабатывание ни к False Positive (неизвестно, что этот диск еще проживет, допустим, минимум дней), ни к True Positive (нет информации о реальном времени отказа диска).

Справедливости ради, таких случаев немного, и они просто исключаются из подсчета Precision и Recall.

Про метрики поговорили. Теперь о том, какие их значения достижимы.

Наш прототип на основе существующей модели

В таблице ниже подсвечены результаты команды участников полуфинала PAKDD2020, занявшей третье место.

Ранее мы уже писали, что разработанная ими модель легла в основу нашего прототипа. Поэтому мы ожидали, что получим максимально близкие к этим цифрам результаты. Спойлер: в реальности вышло иначе. Тем не менее, это некий baseline, от которого мы отталкивались. В терминах ранее описанных k-window метрик равно 30 дням, а значение Best Before равно нулю.

Чем наш прототип отличается от модели китайской команды

Мы заметили, что модель, разработанная бронзовыми призерами PAKDD2020, обладает рядом недостатков:

Она тренировалась и тестировалась на дисках с одними и теми же серийными номерами. Иными словами, модель тестировалась на тех же дисках, на которых обучалась.
Модель не универсальна с точки зрения интервала времени, на котором она будет применяться. Модель тренировалась на данных за текущий месяц и использовалась на данных в пределах следующего месяца.

А значит, потенциальному пользователю модели китайских разработчиков — например, какой-нибудь компании — пришлось бы создавать систему регулярного сбора SMART-данных и данных о событиях отказов. Это существенно снижает возможность широкого использования модели.

Мы задались целью понять, можно ли для фиксированной модели жесткого диска построить универсальную модель, прогнозирующую выход HDD из строя. И которую можно было бы применять к ранее не виданным дискам в произвольные моменты времени. При этом не потерять в качестве прогнозирования.

Как выглядит наш прототип

В нашем прототипе модели прогнозирования проблем HDD мы использовали алгоритм LightGBM classifier, так как это задача бинарной классификации. Это алгоритм из семейства GBDT (Gradient-Boosted Decision Trees), которое является золотым стандартом алгоритмов ML для обработки табличных данных (наряду с аналогичными XGBoost, CatBoost и другими).

На вход мы подавали два вида признаков:

сырые значения SMART-атрибутов,
«производные» признаки — абсолютные разницы в значениях SMART-атрибута на текущий момент и сутки назад, сглаженные экспоненциально взвешенным скользящим средним на окне в 7 дней.

Целевая метка (target label) при обучении классификатора называлась failure_in_{backtrace_days}d. Backtrace составлял 7 дней. Буквально эта метка означала, что ожидается отказ диска в ближайшие семь дней, но в датафрейме это выглядело как единица на последней неделе жизни HDD.

Один из важных моментов, который мы позаимствовали у прототипа модели, — это функция потерь focal loss, которая используется при обучении. В основном она применяется в задаче обнаружения объектов на изображениях, но неплохо себя показывает и в задаче классификации, когда есть сильный дисбаланс классов. А в нашей задаче он присутствует: процент отказавших дисков, как правило, составляет 1% или меньше.

Алгоритм LightGBM на выходе выдает вероятность принадлежности к классу. В нашем случае это означает вероятность выхода из строя в ближайшем будущем. Логика формирования предсказанной метки у нас простая: если вероятность отказа выше определенного порога, то диску присваивается метка 1 (он выйдет из строя в ближайшее время). В противном случае присваивается метка 0 — диск здоров.

На самом деле, значение порога можно задать руками, но мы его оптимизировали на валидационной выборке, чтобы максимизировать метрику F1.

Полученная LightGBM-модель заворачивается в адаптер, формирующий «производные» признаки, которые мы упоминали ранее, перед вызовом predict_proba(). Благодаря этому адаптеру также не нужно самостоятельно считать эти признаки.

Все это соответствует нашей задаче, что по короткой истории SMART-данных получится сформировать прогноз. Так, в текущей имплементации адаптера нужно передать семь последовательных снимков состояния, которые были сделаны с интервалом в 24 часа, — модель сделает прогноз вида «выйдет диск из строя или нет в ближайшем будущем».

Результаты проверки модели, которые мы получили

Для экспериментов мы отобрали ряд моделей дисков, которые близки к тем, что используются в серверах YADRO. Точных совпадений в датасете BackBlaze найти не удалось, поэтому мы ориентировались на схожие характеристики — например, на скорость вращения шпинделя. Также нам было важно, чтобы для выбранных дисков было достаточно данных для обучения. Достаточно — это хотя бы несколько сотен событий отказов.

В итоге в нашем эксперименте участвовали пять моделей дисков от Seagate: ST8000NM0055, ST12000NM0008, ST14000NM001G, ST16000NM001G, ST4000DM000.

Скрытый текст

В таблице перечислены SMART-атрибуты, которые мы использовали для формирования прогноза. Половина из них — счетчики ошибок или релоцированных секторов.

Результаты прогнозов получились довольно близкими к установленному нами baseline — цифрам команды, занявшей третье место в конкурсе от Alibaba. Напомним, что это F1 — 40.47, Precision — 52.76, Recall — 32.82.

Для еще большей иллюстративности в последней строчке мы указали показатели команды-победителя PAKDD2020.

Видно, что нам удалось достигнуть достаточно высокой точности с моделью ST14000. Но тот же Recall не превышает 50%, то есть как минимум половину отказов отлавливать не удается.

Также по каждой модели HDD мы получили Test Report в виде графиков. Давайте посмотрим, какие данные мы получили для жесткого диска Seagate Exos X16 (модель ST14000NM001G).

Эти два графика показывают, какие бы значения метрик Precision, Recall и F1 были при другом значении . Например, мне — потенциальному пользователю модели — нужно понять, какие метрики ожидать, если я хочу видеть позитивный прогноз не раньше чем за 10 дней. На графике справа — абсолютные значения из Confusion Matrix, то есть сколько всего True Positive, False Positive, False Negatives и Unknown Positives было.

На этих графиках, опять же, выбранные нами метрики, но при зафиксированном значении . Здесь меняется порог вероятности, по которому мы принимаем решение, делать ли прогноз, что диск выйдет из строя в ближайшем будущем, или нет. Это позволяет понять, в какую сторону этот порог можно скорректировать. Например, в данном случае можно увеличить Precision за счет более высокого значения порога вероятности.

Гистограмма времени доживания

Эта гистограмма — так называемая гистограмма времени доживания. Здесь показано распределение времени между первым позитивным прогнозом модели и реальным выходом диска из строя. Опять-таки, конкретно для выбранной модели жесткого диска.

Для ST14000 можно сказать, что после первого позитивного прогноза для половины дисков выход из строя произойдет в течение 8 дней. Для других моделей дисков случались единичные выбросы в районе 100-200 дней. Это говорит о том, что некоторые признаки выхода из строя проявляются в SMART-данных достаточно рано, но, тем не менее, не означают скорый выход из строя.

Медианное время доживания диска в зависимости от порога принятия решения

На этом графике изображено медианное время доживания диска в зависимости от порога принятия решения. Ранее мы приводили пример, когда можно увеличить значение порога и тем самым выиграть Precision, пожертвовав метрикой Recall. На медианном времени доживания это отобразится линейным уменьшением.

При текущем пороге можно рассчитывать, что до отказа будет оставаться примерно одна неделя. А при уменьшении порога вероятности до 0.6 получим примерно 20 дней на то, чтобы среагировать, но пожертвуем точностью прогноза.

Выводы

Нам удалось построить прототип модели, которая не зависит от момента времени исполнения и конкретного экземпляра дисков. В нашей тестовой выборке были экземпляры дисков с серийными номерами, которых не было в тренировке, но это практически не повлияло на качество прогнозирования. Поэтому мы считаем, что у нас получилась довольно универсальная модель — критической зависимости от данных SMART-дисков, используемых в конкретной компании, нет. А значит, наша модель не требует сложной системы регулярного сбора SMART-данных и событий отказа дисков. В этом ее основная ценность.

Однако есть нюанс: написать одну универсальную модель прогнозирования выхода из строя HDD, применимую к разным моделям дисков, не получится. Для каждой модели диска все же придется обучать отдельную модель. Хотя бы потому, что у каждой модели диска может быть отличный от других набор SMART-атрибутов. Кроме того, износ в SMART-атрибутах проявляется индивидуально для каждой модели диска.

Модели, которые мы разработали на данный момент, могут обеспечивать достаточно высокую точность. В некоторых случаях Precision достигает 70%, но при этом значительное количество отказов эти модели спрогнозировать не могут. Метрика Recall ни разу не превысила 50%, то есть половина дисков выходит из строя по причинам, которые модель не понимает. Эти выходы из строя (отказы) можно назвать «внезапными смертями», или sudden death. Вероятно, столь большое количество «внезапных смертей» говорит о том, что SMART-данных просто недостаточно. И то, что у победителей конкурса от Alibaba, Recall составляет лишь 40%, подтверждает эту гипотезу.

Поэтому для развития этого проекта мы планируем искать новые данные.

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5 сент. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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06.09.24 01:44 Celinagarcia

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23.09.24 18:56 matthewshimself

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01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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