Chain: tasks and channels for reliable intermittent programs

Abstract

Energy harvesting computers enable general-purpose computing using energy collected from their environment. Energy-autonomy of such devices has great potential, but their intermittent power supply poses a challenge. Intermittent program execution compromises progress and leaves state inconsistent. This work describes Chain: a new model for programming intermittent devices. A Chain program is a set of programmer-defined tasks that compute and exchange data through channels. Chain guarantees forward progress at task granularity. A task is restartable and never sees inconsistent state, because its input and output channels are separated. Our system supports language features for expressing advanced data exchange patterns and for encapsulating reusable functionality. Chain fundamentally differs from state-of-the-art checkpointing approaches and does not incur the associated overhead. We implement Chain as C language extensions and a runtime library. We used Chain to implement four applications: machine learning, encryption, compression, and sensing. In experiments, Chain ensured consistency where prior approaches failed and improved throughput by 2-7x over the leading state-of-the-art system.

Sensing, Computing, and Communications for Energy Harvesting IoTs: A Survey

“…We survey these works in this section. [105] Task-based checkpointing-versioning, × × manual idempotency analysis Ratchet [106] Task-based checkpointing, × × automatic idempotency analysis Chain [107] Idempotency task programming, × channel-based data exchange HarvOS [108] CFG-based checkpoint placement, × × × advanced ADC interrupt Clank [109] Dynamic idempotency task decomposition, × checkpointing and versioning Alpaca [110] Idempotency task programming, × privatization data exchange Mayfly [111] Idempotency task programming, remanence timekeeping A. Checkpointing Optimizations 1) Checkpoint placement and activation: refers to the scheme of inserting potential checkpoints to the program at compile-time and activate checkpointing processes at run-time. Inserting checkpoints at different locations of the program leads to distinct checkpointing overhead (including energy and memory) as the number of variables to be saved varies.…”

“…Second, the execution just follows a task flow and no checkpointing is needed between tasks, such as Chain [107] and Alpaca [110]. Instead of decomposing a long program that is written line-by-line (or instruction-by-instruction), Chain and Alpaca designed a new programming model where a program is written at the granularity of a task, i.e., groups of instructions, and these tasks are connected through a control flow graph.…”

IRC Cross-Layer Design Exploration of Intermittent Robust Computation Units for IoTs

“…However, this results in the full availability of the device only when power interruption is unlikely, which can incur relatively long sleeping periods due to the inevitable power outages in many energy harvesting-powered systems. Chain [10] is another model for programming intermittent devices, in which forward-progress is ensured at the task granularity level. It utilizes idempotent processing concepts to make tasks restartable that never experience inconsistency to keep NVM consistent.…”

Intermittent Learning

“…The fundamental difference between a machine learning task and a typical task on a batteryless system (e.g., sensing and executing an offline-trained classifier) lies in the data and application semantics, which requires special treatment for effective learning under an extreme energy budget. Existing works on intermittent computing address important problems, such as ensuring atomicity [14,57], consistency [14,56,57], programmability [35], timeliness [35], and energy-efficiency [7,16,33], which enable efficient code execution of general-purpose tasks. Our work complements existing literature and specializes in a batteryless system on efficient and effective on-device learning by explicitly considering the utility of sensor data and the execution order of different modules of a machine learning task.…”

“…We provide a programming model that allows a programmer to develop an intermittent learning application that executes correctly on an intermittent system. Like many existing proposals, we adopt a task-based -which we call action-based -intermittent programming model [14,15,35,56,57,87]. We provide application programmers with an energy pre-inspection tool that helps them split an existing application code into sub-modules called actions that atomically run to completion on intermittently-powered systems.…”