Elastic and Predictive Allocation of Computing Tasks in Energy Harvesting IoT Edge Networks
ABSTARCT :
We consider a distributed IoT edge network whose end nodes generate computation jobs that can be processed locally or be offloaded, in full or in part, to other IoT nodes and/or edge servers having the necessary computation and energy resources. That is, jobs can either be partitioned and executed at multiple nodes (including the originating node) or be atomically executed at the designate server. IoT nodes and servers harvest ambient energy and jobs have a completion deadline. For this setup, we are concerned with the temporal allocation of jobs that maximizes the minimum level among all energy buffers in the network while meeting all the deadlines, i.e., that makes the network as much as possible energy neutral. Jobs continuously and asynchronously arrive at the IoT nodes, and computing resources are allocated dynamically at runtime, automatically adapting the processing load across nodes and servers. To achieve this, we present a Model Predictive Control based algorithm, where the job scheduler solves a sequence of low complexity convex problems and exploits future job and energy arrival estimates. The proposed technique is numerically evaluated, showing excellent adaptation capabilities, and performance close to that of an offline optimal scheduler with perfect information of all processes.
EXISTING SYSTEM :
? It is quite a challenge for 5G networks to meet URLLC specifications and this will entail major changes to the system architecture of the existing telecom infrastructure.
? Mobile edge computing can then complement the goals of the access network to solve existing challenges including quality of service/experience, security, and power consumption as part of the necessary network transformation.
? Both architectures are designed to perform real-time analytics for traffic engineering and monitoring alongside existing protocols, such as BGP.
? In addition, the coexistence of eMBB and URLLC with different service requirements is also a challenge .
DISADVANTAGE :
? In this paper, we address the task allocation problem which allocates and schedules a set of tasks represented by a task graph to a set of geographically distributed sensor nodes to achieve an overall system objective.
? Therefore, the dynamic resource and task allocation for energy harvesting wireless sensor networks (EH-WSNs) are required, presenting a new set of problems in the area of networking and communication.
? However, in EH-WSNs, due to the fluctuating energy sources, the energy availability profile is uncertain, making task allocation a challenging problem.
? Such problem is quite different from the task allocation problem tackled by those works, which instead focus on individual task scheduling at the node level.
PROPOSED SYSTEM :
• In recent years, a series of works have been proposed to optimize the performance fog computing systems from various aspects.
• By formulating it as a stochastic network optimization problem, we proposed PORA, an efficient online scheme that exploits predictive offloading to minimize power consumption with queue stability guarantee.
• By exploiting their unique structures, we propose PORA, an efficient scheme that exploits predictive scheduling to make decisions in an online manner.
• ETSI proposes a platform that creates a multi-access edge system, which uses several heterogeneous access technologies, such as those proposed by 3GPP, and local or external networks, among others.
ADVANTAGE :
? The performance of our proposed algorithms in terms of the scheduling length and fairness in the energy-driven task mapping objectives is evaluated through simulation.
? Generally, two design considerations for energy harvesting systems are maximizing performance and ensuring energy-neutral operation.
? To evaluate the performance of the proposed algorithm, simulations are carried out based on the available solar irradiance data from the solar energy received at different times of day and night.
? In order to validate the performance of our approach, more complex system parameters such as the number of tasks and precedences in DAG and number of sensor nodes are considered.
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