Power efficiency is an important design issue in mobile devices with limited power supplies. In this paper, we study a reward-based packet scheduling problem in wireless environments. We consider a general scenario in which a transmitter communicates with multiple receivers periodically. To guarantee timely transmission of data, each packet is associated with a delay constraint. The periodic data streams have different importance levels, power functions, and levels of data sizes. The more data a transmitter delivers, the more rewards it obtains. Our objective is to develop schemes that selectively transmit data streams of different data sizes at different transmission rates so that the system reward can be maximized under given time and energy constraints. We show that the problem is NP-hard and develop a dynamic programming algorithm for the optimal solution in pseudopolynomial time. A fast polynomial-time heuristic approach based on clustering of states in state space is presented to achieve close approximation. Simulation results demonstrate the effectiveness of the optimal solution and show that the proposed polynomial-time approach can achieve near-optimal results. Both approaches make a significant improvement over other approaches adapted from existing studies at a marginal runtime overhead.

Power efficiency is an important design issue in embedded devices with limited power supplies. In this paper, we study a reward-based packet scheduling problem in wireless environments. We consider a general scenario in which a transmitter communicates with multiple receivers periodically. To guarantee timely transmission of data, each packet is associated with a delay constraint. The periodic data streams have different importance levels, power functions, and levels of data sizes. The more data a transmitter delivers, the more rewards it obtains. Our objective is to develop schemes that selectively transmit data streams of different data sizes at different transmission rates to maximize the system reward under given time and energy constraints. We show that this problem is NP-hard and develop a dynamic programming algorithm for the optimal solution in pseudo-polynomial time. A fast polynomial-time heuristic approach is presented to achieve close approximation. Simulation results demonstrate that the proposed heuristic approach can achieve a significant improvement over other approaches adapted from existing studies at a small runtime overhead.

We study online energy-efficient packet scheduling in a multi-user AWGN channel. The objective is to minimize the overall energy consumption in both transmission and circuit by adapting packet transmission rates with individual packet delay constraints. The scheduling is online in the sense that the transmission rates are determined without any assumption about future packet arrivals. We consider a transmitter with a general input and the transmitter communicates with multiple receivers with different distances, which means packets may have different power characteristics and delay constraints. We propose to conduct online rate assignment based on backlogged packets and derive the optimal algorithm in transmitting the packets. We compare the proposed scheduler with existing online algorithms via simulation in terms of energy consumption and algorithms efficiency. Results demonstrate the effectiveness of the online algorithms in striking a better energy-delay trade-off.

We present a dynamic voltage scaling (DVS) technique that minimizes system-wide energy consumption for both periodic and sporadic tasks. It is known that a system consists of processors and a number of other components. Energy-aware processors can be run in different speed levels; components like memory and I/O subsystems and network interface cards can be in a standby state when they are active, but idle. Processor energy optimization solutions are not necessarily efficient from the perspective of systems. Current system-wide energy optimization studies are often limited to periodic tasks with heuristics in getting approximated solutions. In this paper, we develop an exact dynamic programming algorithm for periodic tasks on processors with practical discrete speed levels. The algorithm determines the lower bound of energy expenditure in pseudopolynomial time. An approximation algorithm is proposed to provide performance guarantee with a given bound in polynomial running time. Because of their time efficiency, both the optimization and approximation algorithms can be adapted for online scheduling of sporadic tasks with irregular task releases. We prove that system-wide energy optimization for sporadic tasks is NP-hard in the strong sense. We develop (pseudo-) polynomial-time solutions by exploiting its inherent properties.

Energy efficiency is an important factor in embedded systems design. We consider an embedded system with a dynamic voltage scaling (DVS) capable processor and its system-wide power consumption is dominated by the processor and memory. We present speed assignment polices for a set of periodic/aperiodic tasks that minimize the overall system energy consumption including active and idle power of CPU and other components. A limitation of most DVS-based system-wide energy optimization techniques is that they assume the number of worst-case execution cycles (WCEC) of a task is a constant, independent of CPU frequency. This is not the case when other system components such as memory are taken into account. In this paper, we decompose task execution time into two parts: on-chip inside CPU and off-chip outside the CPU. We propose a frequency-aware system-wide energy minimization approach and establish necessary and sufficient conditions for the optimality. By exploiting properties of the conditions, we derive a bisection algorithm that finds the optimal solution to offline periodict asks in a linear time complexity. We apply a similar analytical approach to online aperiodic tasks scheduling and devise an iterative speed assignment algorithm in the complexity of O(n2). We prove it is optimal among all online algorithms without assumptions about future task releases.

Dynamic voltage scaling (DVS) is a promising technique for battery-powered systems to conserve energy consumption. Most existing DVS algorithms assume information about task periodicity or a priori knowledge about the task set to be scheduled. This paper presents an analytical model of general tasks for DVS assuming job timing information is known only after a task release. It models the voltage scaling process as a transfer function-based filtering system, which facilitates the design of two efficient scaling algorithms. The first is a time-invariant scaling policy and it is proved to be a generalization of several popular DVS algorithms for periodic, sporadic, and aperiodic tasks. A more energy efficient policy is a time-variant scaling algorithm for aperiodic tasks. It is optimal in the sense that it is online without assumed information about future task releases. The algorithm turns out to be a water-filling process with a linear time complexity. It can be applied to scheduling based on worst-case execution times as well as online slack distribution when jobs complete earlier. We further establish two relationships between computation capacity and deadline misses to provide a statistical real-time guarantee with reduced capacity

In this paper, we study the problem of packet scheduling in a wireless environment with the objective of minimizing the average transmission energy expenditure under individual packet delay constraints. Most past studies assumed that the input arrivals followed a Poisson process or were statistically independent. However, traffic from a real source typically has strong time correlation. We model a packet scheduling and queuing system for a general input process in linear time-invariant systems. We propose an energy-efficient packet scheduling policy that takes the correlation into account. Meanwhile, a slower transmission rate implies that packets stay in the transmitter for a longer time, which may result in unexpected transmitter overload and buffer overflow. We derive the upper bounds of the maximum transmission rate under an overload probability and the upper bounds of the required buffer size under a packet drop rate. Simulation results show that the proposed scheduler improves up to 15 percent in energy savings compared with the policies that assume statistically independent input. Evaluation of the bounds in providing QoS control shows that both deadline misses and packet drops can be effectively bounded by a predefined constraint.

We present a dynamic voltage scaling (DVS) technique that minimizes system-wide energy consumption for both periodic and sporadic tasks. It is known that a system consists of processors and a number of other components. Energy-aware processors can be run in different speed levels; components like memory and I/O subsystems and network interface cards can be in a standby state when they are active but idle. Processor energy optimization solutions are not necessarily efficient from the perspective of systems. Current system-wide energy optimization studies are often limited to periodic tasks with heuristics in getting approximated solutions. In this paper, we develop an exact dynamic programming algorithm for periodic tasks on processors with practical discrete speed levels. The algorithm determines the lower bound of energy expenditure in pseudo-polynomial time. An approximation algorithm is proposed to provide performance guarantee with a given bound in polynomial running time. Because of their time efficiency, both the optimization and approximation algorithms can be adapted for online scheduling of sporadic tasks with irregular task releases. We prove that system-wide energy optimization for sporadic tasks is NP-hard in the strong sense. We develop (pseudo-) polynomial-time solutions by exploiting its inherent properties.

This paper considers packet scheduling with delay constraint that minimizes the average transmission energy expenditure in a wireless environment. There were recent studies that modeled transmission rate as a linear combination of input packet arrivals in the derivation of optimal scheduling policies for independent input arrivals. In this paper, we extend the scheduling policies in two aspects. We first consider the impact of input time-correlation and derive an optimal scheduling policy for energy minimization. It is observed that existing energy optimized scheduling policies save energy by transmitting packets slowly. They may result in unexpected deadline misses. We establish a relationship between the maximum reliable transmission rate and a QoS constraint, characterized by a deadline miss rate. The relationship facilitates the trade-off study of power consumption and delay constraint. Simulation results verify the relationship and show the superiority of the scheduler in comparison with the existing approaches.

The increasing popularity of high-volume performance-critical Internet applications is a challenge for servers to provide individual response-time guarantees. Considering the fact that most Internet applications can tolerate a small percentage of deadline misses, we define delay constraint as a statistical guarantee to relax server resource requirements. A recent decay function model characterizes the relationship between the request delay constraint, deadline misses, and server capacity in a transfer function based filter system. A time-invariant scheduler was proposed to minimize system load variances in support of requests with the same delay constraints. This paper extends the model to support requests with different deadlines and describes an optimal time-variant scheduling policy that minimizes load variances and capacity requirement. The resultant capacity bound is further tightened by utilizing the information of request arrival distribution. Simulation results validate the extended decay function model and show the superiority of the scheduler in comparison with other scheduling algorithms.

Dynamic voltage scaling (DVS) is a promising technique for battery-powered systems to conserve energy consumption. Most existing DVS algorithms assume information about task periodicity or a priori knowledge about the task set to be scheduled. This paper presents an analytical model of general tasks for DVS assuming job timing information is known only after a task release. It models the voltage scaling process as a transfer function-based filter system, which facilitates the design of two efficient scaling algorithms. The first is a time-invariant scaling policy based on a voltage scaling function independent of input jobs over time. It is proved to be a generalization of several existing DVS algorithms. A more energy efficient policy is a time-variant scaling algorithm. The algorithm turns out to be a water-filling process of information theory with a low time complexity. It can not only be applied to scheduling based on worst case execution times, but also to online slack distribution when jobs complete earlier. We further establish two relationships between computation capacity and deadline misses. The relationships make it possible to the provisioning of statistical real-time guarantees.

With the increasing popularity of network applications, mobile codes become a promising technology to provide scalable services. Due to their mobile nature, it is a challenge to support synchronous transient communication between mobile objects. This work presents a reliable connection migration mechanism that allows mobile objects in communication to remain connected during their migration. This mechanism supports concurrent migration of both end-points of a connection and guarantees exactly-once delivery of all transmitted data. In addition, a mobile code access control model is integrated to ensure secure connection migration. This work presents the design of the mechanism and a reference implementation, namely NapletSocket, over Java Socket in a mobile agent system. Experimental results show that NapletSocket incurs a moderate cost in connection setup, mainly due to security checking, and marginal overhead for communication over established connections. Furthermore, we investigate the impact of agent mobility on communication performance via simulation. Simulation results show that NapletSocket is efficient for a wide range of migration and communication patterns.