Serverless computing has become more popular and evolved to support more complex tasks than the original Function as a Service (FaaS) model. The design of serverless systems has advanced to accommodate application demands and offer flexibility. Careful characterization of modern serverless systems and understanding of current gaps are warranted. Publicly available datasets on workloads in select production serverless systems do not fully represent all offerings or capture traces at the required time resolution to identify changes in application-level request-response patterns.

This paper presents a new tri-level optimization approach to financial interaction the local electricity market (LEM) with prosumers and the wholesale electricity market (WEM) based on transactive energy (TE) concepts under uncertainty. In the first level of the problem, prosumers are modeled as separate microgrids, which use smart vehicle charging strategies with high penetration of renewable energy sources (RESs) to minimize expected operation costs by submitting offers/bids to the LEM. The LEM also seeks to minimize its expected operating costs at the second level problem by considering the physical constraints of the electricity distribution network (EDN) and by using demand response programs (DRPs). At the third level problem, the WEM, considering the physical constraints of the transmission network (TN) and the high penetration of wind farms, seeks to market-clearing for maximized social welfare. The equilibrium of the proposed tri-level problem is proved by applying a new hybrid Karush–Kuhn–Tucker (KKT) & two-stage iterative-based method. A moment-based distributionally robust optimization (DRO) algorithm has been developed to provide a robust solution against the uncertain behavior of RESs in prosumers and wind farms in the WEM based on the TE concepts. The case studies are simulated by three networked microgrids, a standard 33-bus EDN test system and a standard 6-bus transmission test system. The results of the case studies show that the proposed TE-based framework can effectively coordinate the optimal scheduling of all three main levels, namely prosumers, LEM and WEM, to reduce expected operating costs significantly.

The accelerating interest in energy system models has influenced numerous revolutionized alterations in the wholesale gas and electricity market. This article proposes a novel bilevel multifollower optimization framework for the strategic behavior observation of an integrated (thermal and electrical) energy service provider (IESP) as a pricemaker in the wholesale electricity market (WEM) and natural gas market (NGM). At the upper level, the IESP submits offers/bids in WEM and NGM to procure electricity/gas to the customers. To this end, the IESP endeavors to minimize operational costs and influences the market-clearing price (MCP) by deploying demand-side flexibilities, i.e., elastic electrical and thermal loads. At the lower level, the WEM operator and NGM operator receive offers/bids from all market participants and clear the market with the goal of maximizing social welfare. The IESP is modeled via IEEE-33 bus active distribution system and an 8-node district heating system, whereas the WEM and NGMs are embodied by a 6-bus transmission network and a 21-node natural gas network, respectively. Karush–Kuhn–Tucker conditions are introduced to transform the multifollower bilevel optimization problem into a single-level problem, whereas the inherent nonlinearities of the problem are linearized using the theory of strong duality. Moreover, the intrinsic intermittencies of the renewable energy sources is dealt with by the risk-averse information gap decision theory. The results confirm that flexible electrical and thermal demands can diminish the MCP by as much as 4.1%.

The need to understand job scheduling on devices with intermittent availability is of significant interest today because of the use of battery-powered devices - including electric vehicles - that rely on recharging intervals or energy harvesting. In some recent work by Islam and Nirjon, effective heuristics were proposed for scheduling recurring tasks with deadlines on such intermittently available devices. The broader computational complexity of job scheduling has not been explored in this setting where there is a relationship between job durations and energy consumption. We provide a richer understanding of this problem space. We consider two recharging approaches, one where the battery has to be fully charged during a recharging interval (sometimes considered better for extending battery lifetime) and another where the battery can be partially charged, and we study different scheduling objectives: minimizing the sum of completion times, minimizing the maximum tardiness, and minimizing the number of tardy jobs. We also consider four different relationships between job duration and energy consumption: (i) energy consumption is equal for all jobs irrespective of job length; (ii) job length is equal for all jobs irrespective of energy consumption; (iii) energy consumption is directly proportional to job length; and (iv) there is an arbitrary relationship between job length and energy consumption. In effect, we consider 24 different scheduling problems, and establish that most problems subject to a complete recharging requirement are NP-Hard but that most problems can be solved in polynomial time when partial recharging is permitted. Interestingly, we have been unable to resolve the computational complexity for the one case of minimizing the sum of completion times subject to partial recharging.