Embedded systems serve an emerging and diverse set of applications. As a result, more computational and storage capabilities are added to accommodate ever more demanding applications. Unfortunately, adding more resources typically comes on the expense of higher energy costs. New chip design with Multiple Clock Domains (MCD) opens the opportunity for fine-grain power management within theprocessor chip. When used with dynamic voltage scaling (DVS), we can control the voltage and power of each domain independently. A significant power and energy improvement has been shown when using MCD design in comparison to managing a single voltage domain for the whole chip, as in traditional chips with global DVS.

The combination of increasing component power consumption, a desire for denser systems, and the required performance growth in the face of technology-scaling issues are posing enormous challenges for powering and cooling of server systems. The challenges are directly linked to the peak power consumption of servers.Our solution, Power Shifting, reduces the peak power consumption of servers minimizing the impact on performance. We reduce peak power consumption by using workload-guided dynamic allocation of power among components incorporating real-time performance feedback, activity-related power estimation techniques, and performance-sensitive activity-regulation mechanisms to enforce power budgets.We apply our techniques to a computer system with a single processor and memory. Power shifting adds a system power manager with a dynamic, global view of the system's power consumption to continuously re-budget the available power amongst the two components. Our contributions include:• Demonstration of the greater effectiveness of dynamic power allocation over static budgeting,• Evaluation of different power shifting policies,• Analysis of system and workload factors critical to successful power shifting, and• Proposal of performance-sensitive power budget enforcement mechanisms that ensure system reliability.

Abstract Power conservation has become a key design issue for many sys-tems, including clusters deployed for embedded systems, where

In the context of battery-powered real-time systems three constraints need to be addressed: energy; deadlines; and task rewards. Many future real-time systems will count on different software versions, each with different rewards, time and energy requirements, to achieve a variety of QoS-aware tradeoffs. We propose a solution that allows the device to run the most valuable task versions while still meeting all deadlines and without depleting the energy. Assuming that the battery is rechargeable, we also propose: (a) a static solution that maximizes the system value assuming a worst-case scenario (i.e., worst-case task execution times); and (b) a dynamic scheme that takes advantage of the extra energy in the system when worst-case scenarios do not happen. Three dynamic policies are shown to make better use of the recharging energy while improving the system value.

New technologies have brought about a proliferation of embedded systems, which vary from control systems to sensor networks to personal digital assistants. Many of the portable embedded devices run several applications, which typically have three constraints that need to be addressed: energy , deadline , and reward . However, many of these portable devices do not have powerful enough CPUs and batteries to run all applications within their deadlines. An optimal scheme would allow the device to run the most applications, each using the most amount of CPU cycles possible, without depleting the energy source while still meeting all deadlines. In this paper we propose a solution to this problem; to our knowledge, this is the first solution that combines the three constraints mentioned above. We devise two algorithms, an optimal algorithm for homogeneous applications (with respect to power consumption) and a heuristic iterative algorithm that can also accommodate heterogeneous applications (i.e., those with different power consumption functions). We show by simulation that our iterative algorithm is fast and within 1% of the optimal.

Typical real-time scheduling theory has addressed deadline and energy constraints as well as deadline and reward constraints simultaneously in the past. However we believe that embedded devices with varying applications typically have three constraints that need to be addressed: energy, deadline, and reward. These constraints play important roles in the next generation of embedded devices, since they provide users with a variety of QoS-aware trade-offs. An optimal scheme would allow the device to run the most critical and valuable applications, without depleting the energy source while still meeting all deadlines. In this paper we propose a solution to this problem for typical control systems, such as frame-based task sets. We devise two algorithms that closely approximate the optimal solution while taking only a fraction of the runtime of an optimal solution.